Knowledge Work and the Role of Higher Education in the Age of AI
As AI becomes more and more entangled in various forms of mental work, Bart Verhoeven And Vishal Rana discuss how higher education can adapt to the needs of a changing labor market. Pointing to the limitations of traditional forms of testing in higher education and the benefits of learning by doing and valid assessment, they argue that higher education can change to emphasize more and better skills rather than retention of knowledge.
OpenAI CEO Sam Altman recently warned group of US senators about the potential disruption to the careers of knowledge workers around the world. Large language models (LLMs) such as ChatGPT, BingChat, and Bard demonstrate unrivaled capabilities in many areas. While not without drawbacks (so far), these include but are not limited to data storage, responses to queries, essay writing, reporting, academic articles, policies, strategies, legal documentation and coding. These skills epitomize the experience of knowledge workers around the world. As AI technologies begin to transform our world, it is important that we critically rethink our curriculum, pedagogy and assessment approaches to prepare students for a rapidly changing environment.
Large Language Models (LLMs) ~ demonstrate unrivaled capabilities in many areas. Although not without drawbacks (so far), these include but are not limited to data storage, query responses, essay writing, reports, academic articles, policies, strategies, legal documentation, and coding. These skills epitomize the experience of knowledge workers around the world.
In our recent experience with students using the LLM for educational purposes such as design research, ideation, critical and creative thinking, prototyping, and concept testing, we have observed three different reactions to the inclusion of AI as a learning tool. (1) The vast majority shows pure delight and awe. (2) The smaller group expresses bewilderment and anxiety, voicing concerns such as “What is my role in this new world order?”. (3) Finally, a smaller percentage of students respond palpable fear And indignation to technology, stubbornly refusing to use it. Educators have a responsibility to consider these different responses and ensure that educational systems are robust and adaptive enough to meet the needs of all learners. This includes responsibility for preparing students with higher order skills, such as emotional intelligence, collaboration, creativity, and critical thinking, which are likely to be needed in the future. It requires a transition to empirical learning methods, reliable estimate And AI tutor adaptation enable knowledge workers not only to co-exist with AI, but to thrive with it.
Quality Assurance and Evaluation in the Age of AI
Two examples: In recent discussions with a computer science academic, concerns were raised that freshmen could potentially use ChatGPT to complete assignments. The scientist shared his prediction regarding the ability to identify messages created using ChatGPT. They noted an unusually high level of proficiency in some materials, the lack of expected rookie mistakes for first-year students, suspected the use of ChatGPT, but complained about lack of tools to confirm this.
In conversations with a management scientist, we learned that they tried to circumvent the use of ChatGPT by creating video case studies with industry experts discussing real management issues in their organizations. However, we used a free transcription app. Otter.ai transcribe videos and enter the transcript into ChatGPT, achieving a more than passing score, although not a high one (yet), to demonstrate the vulnerability of video scores to AI fraud. The academic called us “evil”.
After further investigation, ChatGPT itself candidly identified four categories of ratings that could be vulnerable to fraud. (1) Essays or reports: Based on a hint or keyword, ChatGPT can generate related and relevant texts based on data analysis and the use of any template tools such as SWOT analysis or Business Model Canvas. (2) Unsupervised quizzes or tests: including factual or multiple choice questions. ChatGPT-4 is reported to have passed many tertiary examinations and received a score in 90th percentile on the US bar exam. (3) Coding assignments: assignments that require students to write a program or script. ChatGPT can code in multiple languages, allowing students to complete tasks without any programming skills. (4) Creative writing assignments. These tasks require students to compose a story, poem, song, etc. ChatGPT can generate creative texts in a variety of genres and styles based on a prompt or theme. First of all culture of honesty and integrity contributes to excellent learning, but the use The four assessment methods above allow students to do more than just copybut it is also difficult for honest students to use AI to its full potential.
Balance between exams and experiential learning
In order to combat fraud, an ongoing debate has flared up in academia with two prominent points of view: one advocates the value control examinations and other champions experiential learning With reliable estimates, both claim to be effective in combating academic dishonesty. We believe there is room for both perspectives, but we should carefully examine whether traditional exams, which primarily assess retention of knowledge, remain relevant in the age of AI. The exams primarily focus on assessing pre-existing knowledge, but they often fail when it comes to assessing the application of knowledge in the real world, critical thinking, problem solving, collaboration, and communication skills. Qualities that are becoming increasingly important in our rapidly evolving workforce. Despite its vast capabilities, AI currently struggles to capture context or form value judgments, aspects that humans are remarkably good at. People have an inherent ability to interpret complex situations and produce valuable, innovative results that benefit others. In this way, intellectual work can move from managers and creators to editors and facilitators.
AI is currently struggling to capture context or form value judgments, aspects that humans are remarkably good at. People have an inherent ability to interpret complex situations and produce valuable, innovative results that benefit others.
human complement
As AI becomes more and more intricately woven into the fabric of our lives, it is imperative to cultivate future skills in which AI serves as a complement to the human. Take, for example, the field of emotional intelligence. Skills such as empathy, motivation, self-regulation, cooperation, and social ability are paramount for roles that involve understanding and meeting people’s needs and developing compassion. While AI can assist in the idea generation process and produce results based on learned patterns, it struggles with generating truly original, context-sensitive ideas (user-driven innovation), which is clearly a human ability. Similarly, AI complements human efforts in the field complex problem solving. While AI excels at tasks that adhere to established patterns or rules, it falters when confronted with complex problems that require a subtle understanding of context to generate innovative solutions. The skills to confront and solve such problems will continue to be in demand. Other important competencies include ethical judgment and learning ability—the ability to quickly learn new tools, systems, and concepts. A balance between these core human skills and AI capabilities is vital to preparing for a future in which human and artificial intelligence work synergistically to solve the world’s problems.
Leveraging Complementary AI Competencies: Preparing for the Future
In a rapidly evolving academic landscape, the limitations of traditional assessment methods such as essays and question-based reports are becoming increasingly apparent. Supervised examinations, despite their continued relevance to quality assurance, are complemented by a pedagogy that includes experiential learning. This hands-on approach promotes real-world skill development by offering an AI-friendly alternative to traditional lecture-tutorial-exam formats. We suggest going to reliable estimates according to this methodology, such as portfolios, project briefs, and (simulated) real-life tasks. This more resource-intensive approach could disrupt the current higher education business model, but Universities that ignore or ban ChatGPT can hurt their practices. Such a shift will allow future knowledge workers to shine where AI fails, and also allow AI to improve their work. The rise of AI should not be seen as a threat, but as a catalyst for growth, encouraging a symbiosis between human potential and artificial intelligence.
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Image credit: Google DeepMind via Unsplash.com.
